Want to make your LLM more reliable and accurate?
Controlling AI Responses Through API Integration
Want to make your LLM more reliable and accurate? Integrating API calls can bridge the gap between your AI and real-time information. This approach allows LLMs to access up-to-date data and perform specific tasks based on your prompts, making sure their responses are relevant and trustworthy. In this post, we'll briefly explore the benefits of this integration and demonstrate how to implement it using Ollama, an open-source LLM platform.
Why Integrate API Calls with LLMs?
Incorporating API calls allows LLMs to access real-time data and perform specific functions based on user prompts. This integration ensures that the responses are not just generated from the model's training data but are grounded in the most current and relevant information available.
Benefits of This Approach:
Controlled Responses: By defining the APIs and functions the LLM can access, developers can guide the model to produce accurate and relevant outputs, aligning with specific needs and data sources.
Reduced Misinformation: Access to up-to-date information minimizes the chances of the LLM providing incorrect or outdated responses, enhancing the trustworthiness of the interaction.
Enhanced User Experience: Users can make requests in natural language, and the LLM handles the complexity of fetching and processing the necessary data, resulting in precise and helpful responses.
Seamless Integration: This method allows for easy integration with existing systems and workflows, making it a flexible solution for various applications.
Implementing Function Calling in Ollama
Using Ollama, we can define functions that the LLM can call during a conversation. Here's a simplified example illustrating how to set up function calling to perform an API call based on user input:
In this example, the get_activities_sorted function makes an API call to retrieve activities from a process mining log, sorted by the specified criterion. By integrating this function with Ollama, the LLM can invoke it when responding to user prompts that require current data from the process mining log.
Defining the Function for the LLM:
Within Ollama, we specify the function's details so the LLM knows when and how to use it:
How It works in practice:
User Prompt: A user asks, "Can you show me the top 10 activities sorted by frequency?"
LLM Interpretation: The LLM recognizes that this request requires current data and decides to use the get_activities_sorted function.
Function Execution: The function is called with the appropriate parameter (sorting="FREQUENCY"), and it retrieves the data via the API.
Response Generation: The LLM incorporates the fetched data into its response, providing the user with accurate and up-to-date information.
Advantages of this method:
Accuracy and Relevance: Responses are based on the latest data, ensuring that users receive the most accurate information.
Controlled Output: Developers have the ability to define which functions and APIs the LLM can access, leading to more reliable and consistent outputs.
Enhanced Functionality: This integration allows the LLM to perform tasks beyond its training data, such as calculations or data processing, expanding its utility.
Conclusión
By integrating API calls with, we gain significant control over the model's responses, reducing the risk of misinformation. This approach takes advantage of the strengths of LLMs in understanding natural language while making sure that the information provided is accurate and current. It is a powerful way to enhance AI interactions, making them more reliable and useful for a wide range of applications.